R Programming for Data Analysis: Ultimate Guide

R Programming for Data Analysis: Ultimate Guide, available at $54.99, has an average rating of 5, with 109 lectures, based on 8 reviews, and has 30 subscribers.

You will learn about Installing R and R Studio for seamless coding environment setup. Mastering data type conversion and formatting techniques for consistent data representation. Utilizing dplyr functions for efficient data manipulation tasks. Implementing various types of join operations to merge datasets effectively. Aggregating data and engineering new features for insightful analysis. Handling date and time data effectively using lubridate package. Creating customizable visualizations with ggplot2 for effective data communication Complete a capstone project: OpenAirBnB data using concepts and skills learned from this course to create effective visualizations and communicate your findings This course is ideal for individuals who are This course is designed for individuals with no prior experience in tools (e.g., R or Python). or For new graduates who are considering a data analytics career. This course covers real-world practical data analytics use cases and frequently asked interview questions to prepare yourself or For career switchers who wants to be a data analyst or upgrade yourself to perform complex analyses beyond Excel spreadsheets. It is particularly useful for This course is designed for individuals with no prior experience in tools (e.g., R or Python). or For new graduates who are considering a data analytics career. This course covers real-world practical data analytics use cases and frequently asked interview questions to prepare yourself or For career switchers who wants to be a data analyst or upgrade yourself to perform complex analyses beyond Excel spreadsheets.

Enroll now: R Programming for Data Analysis: Ultimate Guide

Summary

Title: R Programming for Data Analysis: Ultimate Guide

Price: $54.99

Average Rating: 5

Number of Lectures: 109

Number of Published Lectures: 109

Number of Curriculum Items: 109

Number of Published Curriculum Objects: 109

Original Price: $109.99

Quality Status: approved

Status: Live

What You Will Learn

Installing R and R Studio for seamless coding environment setup.
Mastering data type conversion and formatting techniques for consistent data representation.
Utilizing dplyr functions for efficient data manipulation tasks.
Implementing various types of join operations to merge datasets effectively.
Aggregating data and engineering new features for insightful analysis.
Handling date and time data effectively using lubridate package.
Creating customizable visualizations with ggplot2 for effective data communication
Complete a capstone project: OpenAirBnB data using concepts and skills learned from this course to create effective visualizations and communicate your findings

Who Should Attend

This course is designed for individuals with no prior experience in tools (e.g., R or Python).
For new graduates who are considering a data analytics career. This course covers real-world practical data analytics use cases and frequently asked interview questions to prepare yourself
For career switchers who wants to be a data analyst or upgrade yourself to perform complex analyses beyond Excel spreadsheets.

Target Audiences

This course is designed for individuals with no prior experience in tools (e.g., R or Python).
For new graduates who are considering a data analytics career. This course covers real-world practical data analytics use cases and frequently asked interview questions to prepare yourself
For career switchers who wants to be a data analyst or upgrade yourself to perform complex analyses beyond Excel spreadsheets.

Interested in becoming a Data Analyst? Want to gain practical skills and solve real-world business problems? Then this is the perfect course for you! This course is created by a Senior Data Analyst who has 10 years of experience in the Insurance and Health Care sectors. This course will equip you with foundational knowledge and help you learn key concepts of loading data, data manipulation, data aggregation, and how to use libraries/packages in a simple method.

I will guide you step-by-step into the World of Data Analysis. With every lecture and lab exercise, you will gain and develop understandings of these concepts to tackle real data problems! This course is mainly designed using R to solve the labs and capstone projects.

This course will be super useful and exciting. I tried my best to design the course curriculum in the most natural logical flow:

· Module 0 – Intro to R: set up R environment and understand the basics of R packages/libraries

· Module 1 – Load and Write Data: learn how to load and write data from flat files (i.e., .csv or Excel format)

· Module 2 – Data Types and Formatting: master the data types and learn how to convert data types for right operations

· Module 3 – Data Manipulation: clean and preprocess data, perform sorting, ordering, and subsetting records

· Module 4 – Join Operations: learn how to perform joins using R packages (i.e., dplyr and sqldf)

· Module 5 – Data Aggregation: learn how to aggregate data using summary statistics and perform feature engineering

· Module 6 – Time Intelligence: learn how to calculate business days and time dimension analysis

· Module 7 – Data Visualization: learn the basics of exploratory data analysis (EDA) and uni-variate/bi-variate visualizations

Each module is independent content. Technically speaking, you can take the course from start to end or jump into any specific topics of your interest. However, I highly recommend students to take the course from Module 1 to 7 in order to complete the capstone project challenge!

This course is packed with real-world data/business problems that I solved during my career as a senior data analyst. You will learn not just concepts but also a lot of practical and hands-on experience from the course. Enroll today and take the first step towards mastering the art of data analysis using R.

Course Curriculum

Chapter 1: Welcome to the Course

Lecture 1: 0_1. Lecture: Part A – Course Intro

Lecture 2: 0_2. Lecture: Part B – Overview of R and R Studio

Lecture 3: 0_3. Lecture: Part C – Install R and Launching R Studio

Lecture 4: 0_4. Lecture: Part D – Intro to R Packages and Installation

Lecture 5: DOWNLOAD COURSE PACK: Datasets, Coding Exercises, Course Outline and Cheatsheet

Lecture 6: 0_5. Demo: Overview of Course Folder Structure

Lecture 7: 0_6. Demo: Part A – How to Download R and R Studio

Lecture 8: 0_7. Demo: Part B – How to Install R

Lecture 9: 0_8. Demo: Part C – How to Install R Studio

Lecture 10: 0_9. Demo: Part D – Navigate R and R Studio

Chapter 2: Load and Write Data in R

Lecture 1: What You Will Learn: Module 1

Lecture 2: 1_1. Lecture: Part A – Summary of Data Objects and Structures

Lecture 3: 1_2. Lecture: Part B – Define Path and Load Data

Lecture 4: 1_3. Lecture: Part C – Write Data

Lecture 5: 1_4. Welcome to Lab 1 Overview

Lecture 6: 1_5. Problem 1: Install R Packages

Lecture 7: 1_6. Problem 2: Define Folder Paths and Setup Directories

Lecture 8: 1_7. Problem 3: Load Data into R Workspace

Lecture 9: 1_8. Problem 4: Write Data into R Workspace

Lecture 10: 1_9. Extra Problem: Capture a Snapshot Date from Filenames

Chapter 3: Data Types and Formatting

Lecture 1: What You Will Learn: Moudle 2

Lecture 2: 2_1. Lecture: Data Types and Data Type Conversion in R

Lecture 3: 2_2. Lecture: Check Column Names and Rename Columns

Lecture 4: 2_3. Lecture: Date Formatting – Year, Month, etc.

Lecture 5: 2_4. Lecture: Character Formatting – Add Leading Zeros

Lecture 6: 2_5. Welcome to Lab 2 Overview

Lecture 7: 2_6. Problem 1: Check Data Types

Lecture 8: 2_7. Problem 2: Rename Columns

Lecture 9: 2_8. Problem 3: Date Formatting

Lecture 10: 2_9. Problem 4: Add Leading Zeros

Chapter 4: Data Manipulation

Lecture 1: What You Will Learn: Module 3

Lecture 2: 3_1. Lecture: Clean Data (drop columns, remove duplicates)

Lecture 3: 3_2. Lecture: Clean Data (recode and replace values)

Lecture 4: 3_3. Lecture: Sort and Order Data

Lecture 5: 3_4. Lecture: Subset Data (Columns, List, Conditions)

Lecture 6: 3_5. Welcome to Lab 3 Overview

Lecture 7: 3_6. Problem 1: Cleaning Data

Lecture 8: 3_7. Problem 2: Recode Data

Lecture 9: 3_8. Problem 3: Replace Data

Lecture 10: 3_9. Problem 4: Arrange Data

Lecture 11: 3_10. Problem 5: Sort Data

Lecture 12: 3_11. Problem 6: Subset Data

Chapter 5: Join Data Operations

Lecture 1: What You Will Learn: Module 4

Lecture 2: 4_1. Lecture: What is Join and Types of Join

Lecture 3: 4_2. Lecture: Perform Joins with dplyr

Lecture 4: 4_3. Lecture: Perform Joins with sqldf

Lecture 5: 4_4. Lecture: Advanced Join Problem – Temporal

Lecture 6: 4_5. Lecture: Advanced Join Problem – Subquery with Max()

Lecture 7: 4_6. Weclome to Lab 4 Overview

Lecture 8: 4_7. Problem 1: Perform Joins with dplyr

Lecture 9: 4_8. Problem 2: Perform Joins with sqldf

Lecture 10: 4_9. Problem 3: Perform Joins on Multiple Tables

Lecture 11: 4_10. Problem 4: Advanced Join Temporal

Lecture 12: 4_11. Problem 5: Advanced Subquery Max()

Lecture 13: 4_12. Extra Problem: Identify Changes in Account Information

Chapter 6: Data Aggregation and Feature Engineering

Lecture 1: What You Will Learn: Module 5

Lecture 2: 5_1. Lecture: Summarize Data (count(), sum(), etc.)

Lecture 3: 5_2. Lecture: Filtering and Slicing Data

Lecture 4: 5_3. Lecture: Convert a Summary Table Format

Lecture 5: 5_4. Lecture: Feature Engineering using mutate()

Lecture 6: 5_5. Welcome to Lab 5 Overview

Lecture 7: 5_6. Problem 1: Summarize Data with dplyr using summarize()

Lecture 8: 5_7. Problem 2: Filter Data with dplyr

Lecture 9: 5_8. Problem 2: Slice Data with dplyr

Lecture 10: 5_9. Problem 3: Sort Data with dplyr

Lecture 11: 5_10. Problem 4: Convert a Summary Table Format

Lecture 12: 5_11. Problem 5: Feature Engineering

Chapter 7: Time Intelligence

Lecture 1: What You Will Learn: Module 6

Lecture 2: 6_1. Lecture: Calculate Time Features using Date Manipulation

Lecture 3: 6_2. Lecture: Calculate Event Sequence Analysis

Lecture 4: 6_3. Lecture: Calculate Number of Business Days

Lecture 5: 6_4. Lecture: Calculate KPIs with Different Frequencies

Lecture 6: 6_5. Welcome to Lab 6 Overview

Lecture 7: 6_6. Problem 1: Date Manipulation – Time Dimension

Lecture 8: 6_7. Problem 1: Date Manipulation – Durations

Lecture 9: 6_8. Problem 2: Calculate Event Sequence Analysis

Lecture 10: 6_9. Problem 3: Calculate Business Days using bizdays Package

Lecture 11: 6_10. Problem 4: Calculate a Measure at Daily Snapshot

Lecture 12: 6_11. Extra Problem: Calculate a Measure at Monthly Snapshot

Chapter 8: Data Visualization with ggplot2

Lecture 1: What You Will Learn: Module 7

Lecture 2: 7_1. Lecture: Intro to Exploratory Data Analysis

Lecture 3: 7_2. Lecture: Uni-Variate: Bar Chart

Lecture 4: 7_3. Lecture: Uni-Variate: Pie Chart

Lecture 5: 7_4. Lecture: Uni-Variate: Line Chart

Lecture 6: 7_5. Lecture: Uni-Variate: Histogram

Lecture 7: 7_6. Lecture: Uni-Variate: Density

Lecture 8: 7_7. Lecture: Bi-Variate: Box Plot

Lecture 9: 7_8. Lecture: Bi-Variate: Scatter Plot

Lecture 10: 7_9. Lecture: Bi-Variate: Correlation Matrix

Lecture 11: 7_10. Lecture: Bi-Variate: Cross Tabulation

Lecture 12: 7_11. Welcome to Lab 7 Overview

Lecture 13: 7_12. Problem 1: Uni-Variate Categorical: Bar Chart

Instructors

Taesun Yoo
Senior Data Analyst | Online Course Instructor

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5 stars: 8 votes

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